Mariner Backtesting - TA-LIB Lowest and highest values over a specified period

MINMAX

 

 min, max = MINMAX(close, timeperiod=30)

Plot

Lowest and highest values over a specified period

Working Example

 from cloudquant.interfaces import Strategy
from collections import OrderedDict
import ktgfunc
import talib

class WE_MINMAX(Strategy):

    def on_start(self, md, order, service, account):
        # symbol and timestamp
        print(self.symbol + ":  " + service.time_to_string(service.system_time))
        daily_bars = md.bar.daily(start=-100)
        close = daily_bars.close
        min, max = talib.MINMAX(close, timeperiod=30)

        # get the date values
        dates = service._context.market._storage.market_hours.keys()
        dateList = []
        for date in dates:
           dateList.append(str(date.strftime('%Y-%m-%d')))
        dates = sorted(dateList, reverse=True)[1:101]
        dates.sort()

        dict = OrderedDict()
        dict['date'] = dates
        dict['close'] = close
        dict['min'] = min
        dict['max'] = max
        symbol = 'MINMAX: ' + self.symbol
        print ktgfunc.talib_table(symbol, 2, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
MINMAX: MSFT
Input Output
date close min max
2016-09-16 56.87 nan nan
2016-09-19 56.55 nan nan
2016-09-20 56.43 nan nan
2016-09-21 57.37 nan nan
2016-09-22 57.43 nan nan
2016-09-23 57.04 nan nan
2016-09-26 56.52 nan nan
2016-09-27 57.56 nan nan
2016-09-28 57.64 nan nan
2016-09-29 57.01 nan nan
2016-09-30 57.21 nan nan
2016-10-03 57.03 nan nan
2016-10-04 56.86 nan nan
2016-10-05 57.25 nan nan
2016-10-06 57.35 nan nan
2016-10-07 57.41 nan nan
2016-10-10 57.65 nan nan
2016-10-11 56.81 nan nan
2016-10-12 56.73 nan nan
2016-10-13 56.54 nan nan
2016-10-14 57.03 nan nan
2016-10-17 56.84 nan nan
2016-10-18 57.27 nan nan
2016-10-19 57.14 nan nan
2016-10-20 56.87 nan nan
2016-10-21 59.26 nan nan
2016-10-24 60.59 nan nan
2016-10-25 60.58 nan nan
2016-10-26 60.22 nan nan
2016-10-27 59.70 56.43 60.59
2016-10-28 59.47 56.43 60.59
2016-10-31 59.52 56.43 60.59
2016-11-01 59.40 56.52 60.59
2016-11-02 59.03 56.52 60.59
2016-11-03 58.81 56.52 60.59
2016-11-04 58.32 56.52 60.59
2016-11-07 60.01 56.54 60.59
2016-11-08 60.06 56.54 60.59
2016-11-09 59.77 56.54 60.59
2016-11-10 58.31 56.54 60.59
2016-11-11 58.62 56.54 60.59
2016-11-14 57.73 56.54 60.59
2016-11-15 58.87 56.54 60.59
2016-11-16 59.65 56.54 60.59
2016-11-17 60.64 56.54 60.64
2016-11-18 60.35 56.54 60.64
2016-11-21 60.86 56.54 60.86
2016-11-22 61.12 56.54 61.12
2016-11-23 60.40 56.54 61.12
2016-11-25 60.53 56.84 61.12
2016-11-28 60.61 56.84 61.12
2016-11-29 61.09 56.87 61.12
2016-11-30 60.26 56.87 61.12
2016-12-01 59.20 56.87 61.12
2016-12-02 59.25 57.73 61.12
2016-12-05 60.22 57.73 61.12
2016-12-06 59.95 57.73 61.12
2016-12-07 61.37 57.73 61.37
2016-12-08 61.01 57.73 61.37
2016-12-09 61.97 57.73 61.97
2016-12-12 62.17 57.73 62.17
2016-12-13 62.98 57.73 62.98
2016-12-14 62.68 57.73 62.98
2016-12-15 62.58 57.73 62.98
2016-12-16 62.30 57.73 62.98
2016-12-19 63.62 57.73 63.62
2016-12-20 63.54 57.73 63.62
2016-12-21 63.54 57.73 63.62
2016-12-22 63.55 57.73 63.62
2016-12-23 63.24 57.73 63.62
2016-12-27 63.28 57.73 63.62
2016-12-28 62.99 58.87 63.62
2016-12-29 62.90 59.20 63.62
2016-12-30 62.14 59.20 63.62
2017-01-03 62.58 59.20 63.62
2017-01-04 62.30 59.20 63.62
2017-01-05 62.30 59.20 63.62
2017-01-06 62.84 59.20 63.62
2017-01-09 62.64 59.20 63.62
2017-01-10 62.62 59.20 63.62
2017-01-11 63.19 59.20 63.62
2017-01-12 62.61 59.20 63.62
2017-01-13 62.70 59.20 63.62
2017-01-17 62.53 59.25 63.62
2017-01-18 62.50 59.95 63.62
2017-01-19 62.30 59.95 63.62
2017-01-20 62.74 61.01 63.62
2017-01-23 62.96 61.01 63.62
2017-01-24 63.52 61.97 63.62
2017-01-25 63.68 62.14 63.68
2017-01-26 64.27 62.14 64.27
2017-01-27 65.78 62.14 65.78
2017-01-30 65.13 62.14 65.78
2017-01-31 64.65 62.14 65.78
2017-02-01 63.58 62.14 65.78
2017-02-02 63.17 62.14 65.78
2017-02-03 63.68 62.14 65.78
2017-02-06 63.64 62.14 65.78
2017-02-07 63.43 62.14 65.78
2017-02-08 63.34 62.14 65.78